Precision Wellness in Silicon Valley has validated a cardiometabolic disease predictive risk engine to accurately predict an individual’s future state of health.
The study was performed as part of sponsored precision medicine research with Stanford University to evaluate nearly 417,000 individuals from a United Kingdom Biobank population that was tracked for six years.
Risk models that were validated include Type 2 diabetes, stroke, coronary artery disease, abdominal aortic aneurysm and deep vein thrombosis.
The economic burden and growing rate of cardiac diseases worldwide is fueling the need for accurate in-time assessments, according to the company.
Importantly for healthcare organizations and other institutions, having accurate insights into individuals’ health can enable risk-bearing companies to get an early look into persons who may be most responsive to early intervention treatment.
“These high-performing risk models have been designed to consider a range of interrelated risk factors and comorbidities,” says Erik Ingelsson, a professor at Stanford University School of Medicine and scientific advisor to Precision Wellness, a scientific wellness and bioanalytics company. “The models are applicable to population analysis as well as individual assessment.”
The risk models cover such factors as genetics, socioeconomic data, lifestyle factors and comorbidities to predict outcomes. This inclusion is important because many patients with cardiometabolic disease do not present their risk when using conventional risk factors, says Mehrdad Rezaee, co-founder of Precision Wellness.
“External validation of risk assessment across large populations is essential for the prescriptive accuracy required for precision health and personalized medicine,” he adds.
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